X-Git-Url: https://gerrit.fd.io/r/gitweb?p=csit.git;a=blobdiff_plain;f=resources%2Ftools%2Fdash%2Fapp%2Fpal%2Ftrending%2Fgraphs.py;h=3b81cf39c4551cbc70723768605ecc6b2f23e2e6;hp=da528a9a0895f6d0d071b1b0e5451680606b2dde;hb=650d20f1fc6bdea669982f2a549744fcdcce5a37;hpb=1996a8e398190a1930607acf72c6f5ce16091e6d
diff --git a/resources/tools/dash/app/pal/trending/graphs.py b/resources/tools/dash/app/pal/trending/graphs.py
index da528a9a08..3b81cf39c4 100644
--- a/resources/tools/dash/app/pal/trending/graphs.py
+++ b/resources/tools/dash/app/pal/trending/graphs.py
@@ -14,50 +14,31 @@
"""
"""
-
-import logging
import plotly.graph_objects as go
import pandas as pd
import re
+import hdrh.histogram
+import hdrh.codec
+
from datetime import datetime
from numpy import isnan
-from dash import no_update
from ..jumpavg import classify
_COLORS = (
- u"#1A1110",
- u"#DA2647",
- u"#214FC6",
- u"#01786F",
- u"#BD8260",
- u"#FFD12A",
- u"#A6E7FF",
- u"#738276",
- u"#C95A49",
- u"#FC5A8D",
- u"#CEC8EF",
- u"#391285",
- u"#6F2DA8",
- u"#FF878D",
- u"#45A27D",
- u"#FFD0B9",
- u"#FD5240",
- u"#DB91EF",
- u"#44D7A8",
- u"#4F86F7",
- u"#84DE02",
- u"#FFCFF1",
- u"#614051"
+ u"#1A1110", u"#DA2647", u"#214FC6", u"#01786F", u"#BD8260", u"#FFD12A",
+ u"#A6E7FF", u"#738276", u"#C95A49", u"#FC5A8D", u"#CEC8EF", u"#391285",
+ u"#6F2DA8", u"#FF878D", u"#45A27D", u"#FFD0B9", u"#FD5240", u"#DB91EF",
+ u"#44D7A8", u"#4F86F7", u"#84DE02", u"#FFCFF1", u"#614051"
)
_ANOMALY_COLOR = {
u"regression": 0.0,
u"normal": 0.5,
u"progression": 1.0
}
-_COLORSCALE = [
+_COLORSCALE_TPUT = [
[0.00, u"red"],
[0.33, u"red"],
[0.33, u"white"],
@@ -65,18 +46,68 @@ _COLORSCALE = [
[0.66, u"green"],
[1.00, u"green"]
]
+_TICK_TEXT_TPUT = [u"Regression", u"Normal", u"Progression"]
+_COLORSCALE_LAT = [
+ [0.00, u"green"],
+ [0.33, u"green"],
+ [0.33, u"white"],
+ [0.66, u"white"],
+ [0.66, u"red"],
+ [1.00, u"red"]
+]
+_TICK_TEXT_LAT = [u"Progression", u"Normal", u"Regression"]
_VALUE = {
"mrr": "result_receive_rate_rate_avg",
"ndr": "result_ndr_lower_rate_value",
- "pdr": "result_pdr_lower_rate_value"
+ "pdr": "result_pdr_lower_rate_value",
+ "pdr-lat": "result_latency_forward_pdr_50_avg"
}
_UNIT = {
"mrr": "result_receive_rate_rate_unit",
"ndr": "result_ndr_lower_rate_unit",
- "pdr": "result_pdr_lower_rate_unit"
+ "pdr": "result_pdr_lower_rate_unit",
+ "pdr-lat": "result_latency_forward_pdr_50_unit"
+}
+_LAT_HDRH = ( # Do not change the order
+ "result_latency_forward_pdr_0_hdrh",
+ "result_latency_reverse_pdr_0_hdrh",
+ "result_latency_forward_pdr_10_hdrh",
+ "result_latency_reverse_pdr_10_hdrh",
+ "result_latency_forward_pdr_50_hdrh",
+ "result_latency_reverse_pdr_50_hdrh",
+ "result_latency_forward_pdr_90_hdrh",
+ "result_latency_reverse_pdr_90_hdrh",
+)
+# This value depends on latency stream rate (9001 pps) and duration (5s).
+# Keep it slightly higher to ensure rounding errors to not remove tick mark.
+PERCENTILE_MAX = 99.999501
+
+_GRAPH_LAT_HDRH_DESC = {
+ u"result_latency_forward_pdr_0_hdrh": u"No-load.",
+ u"result_latency_reverse_pdr_0_hdrh": u"No-load.",
+ u"result_latency_forward_pdr_10_hdrh": u"Low-load, 10% PDR.",
+ u"result_latency_reverse_pdr_10_hdrh": u"Low-load, 10% PDR.",
+ u"result_latency_forward_pdr_50_hdrh": u"Mid-load, 50% PDR.",
+ u"result_latency_reverse_pdr_50_hdrh": u"Mid-load, 50% PDR.",
+ u"result_latency_forward_pdr_90_hdrh": u"High-load, 90% PDR.",
+ u"result_latency_reverse_pdr_90_hdrh": u"High-load, 90% PDR."
}
+def _get_hdrh_latencies(row: pd.Series, name: str) -> dict:
+ """
+ """
+
+ latencies = {"name": name}
+ for key in _LAT_HDRH:
+ try:
+ latencies[key] = row[key]
+ except KeyError:
+ return None
+
+ return latencies
+
+
def _classify_anomalies(data):
"""Process the data and return anomalies and trending values.
@@ -127,184 +158,298 @@ def _classify_anomalies(data):
return classification, avgs, stdevs
-def trending_tput(data: pd.DataFrame, sel:dict, layout: dict, start: datetime,
- end: datetime):
+def select_trending_data(data: pd.DataFrame, itm:dict) -> pd.DataFrame:
"""
"""
- if not sel:
- return no_update, no_update
+ phy = itm["phy"].split("-")
+ if len(phy) == 4:
+ topo, arch, nic, drv = phy
+ if drv == "dpdk":
+ drv = ""
+ else:
+ drv += "-"
+ drv = drv.replace("_", "-")
+ else:
+ return None
+
+ core = str() if itm["dut"] == "trex" else f"{itm['core']}"
+ ttype = "ndrpdr" if itm["testtype"] in ("ndr", "pdr") else itm["testtype"]
+ dut = "none" if itm["dut"] == "trex" else itm["dut"].upper()
- def _generate_traces(ttype: str, name: str, df: pd.DataFrame,
- start: datetime, end: datetime, color: str):
+ df = data.loc[(
+ (data["dut_type"] == dut) &
+ (data["test_type"] == ttype) &
+ (data["passed"] == True)
+ )]
+ df = df[df.job.str.endswith(f"{topo}-{arch}")]
+ df = df[df.test_id.str.contains(
+ f"^.*[.|-]{nic}.*{itm['framesize']}-{core}-{drv}{itm['test']}-{ttype}$",
+ regex=True
+ )].sort_values(by="start_time", ignore_index=True)
- df = df.dropna(subset=[_VALUE[ttype], ])
- if df.empty:
- return list()
+ return df
- x_axis = [d for d in df["start_time"] if d >= start and d <= end]
- anomalies, trend_avg, trend_stdev = _classify_anomalies(
- {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
+def _generate_trending_traces(ttype: str, name: str, df: pd.DataFrame,
+ start: datetime, end: datetime, color: str) -> list:
+ """
+ """
+
+ df = df.dropna(subset=[_VALUE[ttype], ])
+ if df.empty:
+ return list()
+ df = df.loc[((df["start_time"] >= start) & (df["start_time"] <= end))]
+ if df.empty:
+ return list()
+
+ x_axis = df["start_time"].tolist()
+
+ anomalies, trend_avg, trend_stdev = _classify_anomalies(
+ {k: v for k, v in zip(x_axis, df[_VALUE[ttype]])}
+ )
+
+ hover = list()
+ customdata = list()
+ for _, row in df.iterrows():
+ hover_itm = (
+ f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f" [{row[_UNIT[ttype]]}]: {row[_VALUE[ttype]]}
"
+ f""
+ f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"csit-ref: {row['job']}/{row['build']}
"
+ f"hosts: {', '.join(row['hosts'])}"
+ )
+ if ttype == "mrr":
+ stdev = (
+ f"stdev [{row['result_receive_rate_rate_unit']}]: "
+ f"{row['result_receive_rate_rate_stdev']}
"
+ )
+ else:
+ stdev = ""
+ hover_itm = hover_itm.replace(
+ "", "latency" if ttype == "pdr-lat" else "average"
+ ).replace("", stdev)
+ hover.append(hover_itm)
+ if ttype == "pdr-lat":
+ customdata.append(_get_hdrh_latencies(row, name))
+
+ hover_trend = list()
+ for avg, stdev, (_, row) in zip(trend_avg, trend_stdev, df.iterrows()):
+ hover_itm = (
+ f"date: {row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f"trend [pps]: {avg}
"
+ f"stdev [pps]: {stdev}
"
+ f"{row['dut_type']}-ref: {row['dut_version']}
"
+ f"csit-ref: {row['job']}/{row['build']}
"
+ f"hosts: {', '.join(row['hosts'])}"
+ )
+ if ttype == "pdr-lat":
+ hover_itm = hover_itm.replace("[pps]", "[us]")
+ hover_trend.append(hover_itm)
+
+ traces = [
+ go.Scatter( # Samples
+ x=x_axis,
+ y=df[_VALUE[ttype]],
+ name=name,
+ mode="markers",
+ marker={
+ u"size": 5,
+ u"color": color,
+ u"symbol": u"circle",
+ },
+ text=hover,
+ hoverinfo=u"text+name",
+ showlegend=True,
+ legendgroup=name,
+ customdata=customdata
+ ),
+ go.Scatter( # Trend line
+ x=x_axis,
+ y=trend_avg,
+ name=name,
+ mode="lines",
+ line={
+ u"shape": u"linear",
+ u"width": 1,
+ u"color": color,
+ },
+ text=hover_trend,
+ hoverinfo=u"text+name",
+ showlegend=False,
+ legendgroup=name,
)
+ ]
+ if anomalies:
+ anomaly_x = list()
+ anomaly_y = list()
+ anomaly_color = list()
hover = list()
- for _, row in df.iterrows():
- hover_itm = (
- f"date: "
- f"{row['start_time'].strftime('%d-%m-%Y %H:%M:%S')}
"
- f"average [{row[_UNIT[ttype]]}]: "
- f"{row[_VALUE[ttype]]}
"
- f"{row['dut_type']}-ref: {row['dut_version']}
"
- f"csit-ref: {row['job']}/{row['build']}"
- )
- if ttype == "mrr":
- stdev = (
- f"stdev [{row['result_receive_rate_rate_unit']}]: "
- f"{row['result_receive_rate_rate_stdev']}
"
+ for idx, anomaly in enumerate(anomalies):
+ if anomaly in (u"regression", u"progression"):
+ anomaly_x.append(x_axis[idx])
+ anomaly_y.append(trend_avg[idx])
+ anomaly_color.append(_ANOMALY_COLOR[anomaly])
+ hover_itm = (
+ f"date: {x_axis[idx].strftime('%d-%m-%Y %H:%M:%S')}
"
+ f"trend [pps]: {trend_avg[idx]}
"
+ f"classification: {anomaly}"
)
- else:
- stdev = ""
- hover_itm = hover_itm.replace("", stdev)
- hover.append(hover_itm)
-
- hover_trend = list()
- for avg, stdev in zip(trend_avg, trend_stdev):
- hover_trend.append(
- f"trend [pps]: {avg}
"
- f"stdev [pps]: {stdev}"
- )
-
- traces = [
- go.Scatter( # Samples
- x=x_axis,
- y=df[_VALUE[ttype]],
- name=name,
- mode="markers",
- marker={
- u"size": 5,
- u"color": color,
- u"symbol": u"circle",
- },
+ if ttype == "pdr-lat":
+ hover_itm = hover_itm.replace("[pps]", "[us]")
+ hover.append(hover_itm)
+ anomaly_color.extend([0.0, 0.5, 1.0])
+ traces.append(
+ go.Scatter(
+ x=anomaly_x,
+ y=anomaly_y,
+ mode=u"markers",
text=hover,
hoverinfo=u"text+name",
- showlegend=True,
- legendgroup=name,
- ),
- go.Scatter( # Trend line
- x=x_axis,
- y=trend_avg,
- name=name,
- mode="lines",
- line={
- u"shape": u"linear",
- u"width": 1,
- u"color": color,
- },
- text=hover_trend,
- hoverinfo=u"text+name",
showlegend=False,
legendgroup=name,
- )
- ]
-
- if anomalies:
- anomaly_x = list()
- anomaly_y = list()
- anomaly_color = list()
- ticktext = [u"Regression", u"Normal", u"Progression"]
- for idx, anomaly in enumerate(anomalies):
- if anomaly in (u"regression", u"progression"):
- anomaly_x.append(x_axis[idx])
- anomaly_y.append(trend_avg[idx])
- anomaly_color.append(_ANOMALY_COLOR[anomaly])
- anomaly_color.append([0.0, 1.0])
- traces.append(
- go.Scatter(
- x=anomaly_x,
- y=anomaly_y,
- mode=u"markers",
- hoverinfo=u"none",
- showlegend=False,
- legendgroup=name,
- name=f"{name}-anomalies",
- marker={
- u"size": 15,
- u"symbol": u"circle-open",
- u"color": anomaly_color,
- u"colorscale": _COLORSCALE,
- u"showscale": True,
- u"line": {
- u"width": 2
- },
- u"colorbar": {
- u"y": 0.5,
- u"len": 0.8,
- u"title": u"Circles Marking Data Classification",
- u"titleside": u"right",
- u"titlefont": {
- u"size": 14
- },
- u"tickmode": u"array",
- u"tickvals": [0.167, 0.500, 0.833],
- u"ticktext": ticktext,
- u"ticks": u"",
- u"ticklen": 0,
- u"tickangle": -90,
- u"thickness": 10
- }
+ name=name,
+ marker={
+ u"size": 15,
+ u"symbol": u"circle-open",
+ u"color": anomaly_color,
+ u"colorscale": _COLORSCALE_LAT \
+ if ttype == "pdr-lat" else _COLORSCALE_TPUT,
+ u"showscale": True,
+ u"line": {
+ u"width": 2
+ },
+ u"colorbar": {
+ u"y": 0.5,
+ u"len": 0.8,
+ u"title": u"Circles Marking Data Classification",
+ u"titleside": u"right",
+ u"tickmode": u"array",
+ u"tickvals": [0.167, 0.500, 0.833],
+ u"ticktext": _TICK_TEXT_LAT \
+ if ttype == "pdr-lat" else _TICK_TEXT_TPUT,
+ u"ticks": u"",
+ u"ticklen": 0,
+ u"tickangle": -90,
+ u"thickness": 10
}
- )
+ }
)
+ )
- return traces
+ return traces
- # Generate graph:
- fig = go.Figure()
+
+def graph_trending(data: pd.DataFrame, sel:dict, layout: dict,
+ start: datetime, end: datetime) -> tuple:
+ """
+ """
+
+ if not sel:
+ return None, None
+
+ fig_tput = None
+ fig_lat = None
for idx, itm in enumerate(sel):
- phy = itm["phy"].split("-")
- if len(phy) == 4:
- topo, arch, nic, drv = phy
- if drv in ("dpdk", "ixgbe"):
- drv = ""
- else:
- drv += "-"
- drv = drv.replace("_", "-")
- else:
+
+ df = select_trending_data(data, itm)
+ if df is None or df.empty:
continue
- cadence = \
- "weekly" if (arch == "aws" or itm["testtype"] != "mrr") else "daily"
- sel_topo_arch = (
- f"csit-vpp-perf-"
- f"{itm['testtype'] if itm['testtype'] == 'mrr' else 'ndrpdr'}-"
- f"{cadence}-master-{topo}-{arch}"
- )
- df_sel = data.loc[(data["job"] == sel_topo_arch)]
- regex = (
- f"^.*{nic}.*\.{itm['framesize']}-{itm['core']}-{drv}{itm['test']}-"
- f"{'mrr' if itm['testtype'] == 'mrr' else 'ndrpdr'}$"
+
+ name = "-".join((itm["dut"], itm["phy"], itm["framesize"], itm["core"],
+ itm["test"], itm["testtype"], ))
+ traces = _generate_trending_traces(
+ itm["testtype"], name, df, start, end, _COLORS[idx % len(_COLORS)]
)
- df = df_sel.loc[
- df_sel["test_id"].apply(
- lambda x: True if re.search(regex, x) else False
+ if traces:
+ if not fig_tput:
+ fig_tput = go.Figure()
+ fig_tput.add_traces(traces)
+
+ if itm["testtype"] == "pdr":
+ traces = _generate_trending_traces(
+ "pdr-lat", name, df, start, end, _COLORS[idx % len(_COLORS)]
)
- ].sort_values(by="start_time", ignore_index=True)
- name = (
- f"{itm['phy']}-{itm['framesize']}-{itm['core']}-"
- f"{itm['test']}-{itm['testtype']}"
- )
- for trace in _generate_traces(itm['testtype'], name, df, start, end,
- _COLORS[idx % len(_COLORS)]):
- fig.add_trace(trace)
+ if traces:
+ if not fig_lat:
+ fig_lat = go.Figure()
+ fig_lat.add_traces(traces)
+
+ if fig_tput:
+ fig_tput.update_layout(layout.get("plot-trending-tput", dict()))
+ if fig_lat:
+ fig_lat.update_layout(layout.get("plot-trending-lat", dict()))
+
+ return fig_tput, fig_lat
+
+
+def graph_hdrh_latency(data: dict, layout: dict) -> go.Figure:
+ """
+ """
- style={
- "vertical-align": "top",
- "display": "inline-block",
- "width": "80%",
- "padding": "5px"
- }
+ fig = None
+
+ traces = list()
+ for idx, (lat_name, lat_hdrh) in enumerate(data.items()):
+ try:
+ decoded = hdrh.histogram.HdrHistogram.decode(lat_hdrh)
+ except (hdrh.codec.HdrLengthException, TypeError) as err:
+ continue
+ previous_x = 0.0
+ prev_perc = 0.0
+ xaxis = list()
+ yaxis = list()
+ hovertext = list()
+ for item in decoded.get_recorded_iterator():
+ # The real value is "percentile".
+ # For 100%, we cut that down to "x_perc" to avoid
+ # infinity.
+ percentile = item.percentile_level_iterated_to
+ x_perc = min(percentile, PERCENTILE_MAX)
+ xaxis.append(previous_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"Direction: {(u'W-E', u'E-W')[idx % 2]}
"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ next_x = 100.0 / (100.0 - x_perc)
+ xaxis.append(next_x)
+ yaxis.append(item.value_iterated_to)
+ hovertext.append(
+ f"{_GRAPH_LAT_HDRH_DESC[lat_name]}
"
+ f"Direction: {(u'W-E', u'E-W')[idx % 2]}
"
+ f"Percentile: {prev_perc:.5f}-{percentile:.5f}%
"
+ f"Latency: {item.value_iterated_to}uSec"
+ )
+ previous_x = next_x
+ prev_perc = percentile
- layout = layout.get("plot-trending", dict())
- fig.update_layout(layout)
+ traces.append(
+ go.Scatter(
+ x=xaxis,
+ y=yaxis,
+ name=_GRAPH_LAT_HDRH_DESC[lat_name],
+ mode=u"lines",
+ legendgroup=_GRAPH_LAT_HDRH_DESC[lat_name],
+ showlegend=bool(idx % 2),
+ line=dict(
+ color=_COLORS[int(idx/2)],
+ dash=u"solid",
+ width=1 if idx % 2 else 2
+ ),
+ hovertext=hovertext,
+ hoverinfo=u"text"
+ )
+ )
+ if traces:
+ fig = go.Figure()
+ fig.add_traces(traces)
+ layout_hdrh = layout.get("plot-hdrh-latency", None)
+ if lat_hdrh:
+ fig.update_layout(layout_hdrh)
- return fig, style
+ return fig